I. Field
The present invention relates generally to data communication, and more specifically to techniques for deriving eigenvectors used for spatial processing in a multiple-input multiple-output (MIMO) communication system.
II. Background
A MIMO system employs multiple (NT) transmit antennas and multiple (NR) receive antennas for data transmission. A MIMO channel formed by the NT transmit antennas and NR receive antennas may be decomposed into NS spatial channels, where NS≦min {NT, NR}. The NS spatial channels may be used to transmit data in parallel to achieve higher overall throughput or redundantly to achieve greater reliability.
In general, up to NS data streams may be transmitted simultaneously from the NT transmit antennas in the MIMO system. However, these data streams interfere with each other at the receive antennas. Improved performance may be achieved by transmitting data on NS eigenmodes of the MIMO channel, where the eigenmodes may be viewed as orthogonal spatial channels. To transmit data on the NS eigenmodes, it is necessary to perform spatial processing at both a transmitter and a receiver. The spatial processing attempts to orthogonalize the data streams so that they can be individually recovered with minimal degradation at the receiver.
For data transmission on the NS eigenmodes, the transmitter performs spatial processing with a matrix of NS eigenvectors, one eigenvector for each eigenmode used for data transmission. Each eigenvector contains NT complex values used to scale a data symbol prior to transmission from the NT transmit antennas and on the associated eigenmode. For data reception, the receiver performs receiver spatial processing (or spatial matched filtering) with another matrix of NS eigenvectors. The eigenvectors for the transmitter and the eigenvectors for the receiver may be derived based on a channel response estimate for the MIMO channel between the transmitter and receiver. The derivation of the eigenvectors is computationally intensive. Furthermore, the accuracy of the eigenvectors may have a large impact on performance.
There is therefore a need in the art for techniques to efficiently and accurately derive eigenvectors used for data transmission and reception via the eigenmodes of a MIMO channel.